##### Scenario You fit a linear regression with 500 predictors but only 600 observations. ##### Question a) What issue is likely to occur? b) Why does it happen? c) Explain how L1 regularization can mitigate it. ##### Hints Think over-fitting and coefficient shrinkage.

This question evaluates understanding of model overfitting, high-dimensional linear regression issues, and regularization concepts, specifically L1 (Lasso) sparsity and complexity control.

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Address Overfitting with L1 Regularization in Regression

Linear Regression with Many Predictors and Few Observations

Scenario

You fit an ordinary least squares (OLS) linear regression with 500 predictors (features) and 600 observations (rows).

Questions

What issue is likely to occur?
Why does it happen?
How can L1 regularization (Lasso) mitigate it?

Address Overfitting with L1 Regularization in Regression

Quick Overview

Linear Regression with Many Predictors and Few Observations

Scenario

Questions

Solution

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